Yuhang Li
Ph.D. candidate, Electrical and Computer Engineering, UCLA
Bio- and Nano-photonics Lab
UCLA
Los Angeles, CA
I am a Ph.D. candidate in Electrical and Computer Engineering at the University of California, Los Angeles (UCLA), working in the Bio- and Nano-photonics Lab under the mentorship of Prof. Aydogan Ozcan. Before joining UCLA, I received my B.Eng. in Optical Science and Engineering from Zhejiang University in 2021.
My research focuses on computational imaging and optical neural networks. I design diffractive optical processors that compute with light — performing tasks such as quantitative phase imaging, imaging through random diffusers, universal polarization transformations, and nonlinear information processing, all at the speed of light propagation. More recently, I have been exploring optical generative models and model-free training of optical processors using reinforcement learning.
My work has been published in journals including Nature, Science, Light: Science & Applications, Advanced Photonics, Nature Communications, and Science Advances, and has been cited over 1,300 times (h-index: 18).
news
| Jun 01, 2026 | I joined Meta Reality Labs (Redmond, WA) as a Research Scientist Intern for Summer 2026. 🚀 |
|---|---|
| Jan 15, 2026 | Our paper on model-free optical processors using in situ reinforcement learning was published in Light: Science & Applications! 🔦 |
| Aug 27, 2025 | Our work on optical generative models was published in Nature! ✨ |
| Oct 15, 2024 | I was selected as a finalist in the Emil Wolf Outstanding Student Paper Competition at Frontiers in Optics + Laser Science (FiO LS) 2024. |
| Mar 15, 2024 | I advanced to doctoral candidacy at UCLA. 🎓 |
selected publications
- LSAModel-free optical processors using in situ reinforcement learning with proximal policy optimizationLight: Science & Applications, 2026
- LSANonlinear encoding in diffractive information processing using linear optical materialsLight: Science & Applications, 2024
- Light Adv. Manuf.Quantitative phase imaging (QPI) through random diffusers using a diffractive optical networkLight: Advanced Manufacturing, 2023
- Adv. Mater.Universal polarization transformations: Spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformerAdvanced Materials, 2023